




import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


_lowerCamelCase      :						Any							=  re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
_lowerCamelCase      :						int							=  10
_lowerCamelCase      :						Dict							=  256







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Optional[MinHash]:



       """simple docstring"""

       if len(lowercase_		) < MIN_NUM_TOKENS:
              return None
       A__ 					=       MinHash(num_perm=lowercase_		)
       for token in set(lowercase_		):
              min_hash.update(token.encode()		)
       return min_hash







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Set[str]:



       """simple docstring"""

       return {t for t in NON_ALPHA.split(lowercase_		) if len(t.strip()		) > 0}


class 							UpperCamelCase_       :

    '''simple docstring'''






    def __init__(     self :      Tuple   ,					*,
        UpperCAmelCase__ :      float = 0.85   ,					)		->int:





           '''simple docstring'''

           A__ 					=       duplication_jaccard_threshold
           A__ 					=       NUM_PERM
           A__ 					=       MinHashLSH(threshold=self._duplication_jaccard_threshold   ,					num_perm=self._num_perm)

           A__ 					=       defaultdict(UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Any   ,					UpperCAmelCase__ :      Tuple   ,					UpperCAmelCase__ :      MinHash)		->None:





           '''simple docstring'''

           A__ 					=       self._index.query(UpperCAmelCase__)
           if code_key in self._index.keys:
                  print(f"""Duplicate key {code_key}""")
                  return

           self._index.insert(UpperCAmelCase__   ,					UpperCAmelCase__)
           if len(UpperCAmelCase__) > 0:
                  for base_duplicate in close_duplicates:
                         if base_duplicate in self._duplicate_clusters:
                                self._duplicate_clusters[base_duplicate].add(UpperCAmelCase__)
                                break
                  else:
                         self._duplicate_clusters[close_duplicates[0]].add(UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->List[List[Dict]]:





           '''simple docstring'''

           A__ 					=       []
           for base, duplicates in self._duplicate_clusters.items():
                  A__ 					=       [base] + list(UpperCAmelCase__)
                  # reformat the cluster to be a list of dict
                  A__ 					=       [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
                  duplicate_clusters.append(UpperCAmelCase__)
           return duplicate_clusters





    def 			SCREAMING_SNAKE_CASE				(     self :      Any   ,					UpperCAmelCase__ :      Tuple)		->None:





           '''simple docstring'''

           A__ 					=       self.get_duplicate_clusters()
           with open(UpperCAmelCase__   ,					'''w''') as f:
                  json.dump(UpperCAmelCase__   ,					UpperCAmelCase__)







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Optional[Any]:



       """simple docstring"""

       A__					,				A__ 					=       element
       A__ 					=       get_min_hash([t for t in NON_ALPHA.split(data['''content''']		) if len(t.strip()		) > 0]		)
       if min_hash is not None:
              return (index, data["repo_name"], data["path"]), min_hash







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			List[Any]:



       """simple docstring"""

       with mp.Pool() as pool:
              for data in pool.imap_unordered(
                  _compute_min_hash					,							ThreadedIterator(lowercase_					,							max_queue_size=10_000		)					,							chunksize=100					,							):
                     if data is not None:
                            yield data







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Dict:



       """simple docstring"""

       A__ 					=       DuplicationIndex(duplication_jaccard_threshold=lowercase_		)

       for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowercase_		)		)					,							max_queue_size=100		)		):
              di.add(lowercase_					,							lowercase_		)

       # Returns a List[Cluster] where Cluster is List[str] with the filenames.
       return di.get_duplicate_clusters()







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			float:



       """simple docstring"""

       A__ 					=       get_tokens(lowercase_		)
       A__ 					=       get_tokens(lowercase_		)
       return len(tokensa & tokensa		) / len(tokensa | tokensa		)


_lowerCamelCase      :						Any							=  None







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Optional[int]:



       """simple docstring"""

       A__ 					=       []
       for elementa in cluster:
              A__ 					=       _shared_dataset[elementa['''base_index''']]['''content''']
              for elementa in extremes:
                     A__ 					=       _shared_dataset[elementa['''base_index''']]['''content''']
                     if jaccard_similarity(lowercase_					,							lowercase_		) >= jaccard_threshold:
                            elementa["copies"] += 1
                            break
              else:
                     A__ 					=       1
                     extremes.append(lowercase_		)
       return extremes







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			List[str]:



       """simple docstring"""

       global _shared_dataset
       A__ 					=       dataset
       A__ 					=       []
       A__ 					=       partial(_find_cluster_extremes_shared					,							jaccard_threshold=lowercase_		)
       with mp.Pool() as pool:
              for extremes in tqdm(
                  pool.imap_unordered(
                      lowercase_					,							lowercase_					,							)					,							total=len(lowercase_		)					,							):
                     extremes_list.append(lowercase_		)
       return extremes_list







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_ = 0.85		)  ->			Tuple[Type[Dataset], List[List[Dict]]]:



       """simple docstring"""

       A__ 					=       make_duplicate_clusters(lowercase_					,							lowercase_		)
       A__ 					=       {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
       A__ 					=       {}
       A__ 					=       find_extremes(lowercase_					,							lowercase_					,							lowercase_		)
       for extremes in extremes_clusters:
              for element in extremes:
                     A__ 					=       element
       A__ 					=       duplicate_indices - set(extreme_dict.keys()		)
       A__ 					=       dataset.filter(lambda lowercase_					,							lowercase_		: idx not in remove_indices					,							with_indices=lowercase_		)

       # update duplicate_clusters
       for cluster in duplicate_clusters:
              for element in cluster:
                     A__ 					=       element['''base_index'''] in extreme_dict
                     if element["is_extreme"]:
                            A__ 					=       extreme_dict[element['''base_index''']]['''copies''']

       print(f"""Original dataset size: {len(lowercase_		)}"""		)
       print(f"""Number of duplicate clusters: {len(lowercase_		)}"""		)
       print(f"""Files in duplicate cluster: {len(lowercase_		)}"""		)
       print(f"""Unique files in duplicate cluster: {len(lowercase_		)}"""		)
       print(f"""Filtered dataset size: {len(lowercase_		)}"""		)

       return ds_filter, duplicate_clusters










"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						list[list[int | float]]					) ->							int:
  lowerCamelCase_       	=		len(_lowerCamelCase					)
  lowerCamelCase_       	=		len(matrix[0]					)
  lowerCamelCase_       	=		min(_lowerCamelCase						,							_lowerCamelCase					)

  for row in range(_lowerCamelCase					):
    # Check if diagonal element is not zero
    if matrix[row][row] != 0:
      # Eliminate all the elements below the diagonal
      for col in range(row + 1						,							_lowerCamelCase					):
        lowerCamelCase_       	=		matrix[col][row] / matrix[row][row]
        for i in range(_lowerCamelCase						,							_lowerCamelCase					):
          matrix[col][i] -= multiplier * matrix[row][i]
    else:
      # Find a non-zero diagonal element to swap rows
      lowerCamelCase_       	=		True
      for i in range(row + 1						,							_lowerCamelCase					):
        if matrix[i][row] != 0:
          lowerCamelCase_	,      lowerCamelCase_       	=		matrix[i], matrix[row]
          lowerCamelCase_       	=		False
          break
      if reduce:
        rank -= 1
        for i in range(_lowerCamelCase					):
          lowerCamelCase_       	=		matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
      row -= 1

  return rank


if __name__ == "__main__":
  import doctest

  doctest.testmod()




